README.md

---
license: apache-2.0
library_name: fme
---

<img src="ACE-logo.png" alt="Logo for the ACE Project" style="width: auto; height: 50px;">

# HiRO-ACE

The HiRO-ACE framework enables efficient generation of 3 km precipitation fields over decades of simulated climate and arbitrary regions of the globe.
HiRO (High Resolution Output) is a diffusion model which generates downscaled fields at 3 km resolution from 100 km resolution inputs. The HiRO checkpoint included in this model generates 6-hourly averaged surface precipitation rates at 3 km resolution. The Ai2 Climate Emulator (ACE) is a family of models designed to simulate atmospheric variability from the time scale of days to centuries. For usage with the HiRO downscaling model, we include a checkpoint for ACE2S. Compared to previous ACE models, ACE2S uses an updated training procedure and can generate stochastic predictions. For more details, please see the accompanying HiRO-ACE paper linked below.

### Quick links

- 📃 [Paper](https://arxiv.org/pdf/2512.18224)
- 💻 [Code](https://github.com/ai2cm/ace)
- 💬 [Docs](https://ai2-climate-emulator.readthedocs.io/en/stable/)
- 📂 [All ACE Models](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4)


### Inference quickstart

1. Download this repository. Optionally, you can just download a subset of the `forcing_data` and `initial_conditions` for the period you are interested in.

2. Install code dependencies with `pip install fme`.

3. Update paths in the ACE inference config file `ace2s_inference_config_global.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.path`. 

4. Update paths in the HiRO downscaling inference config file `hiro_downscaling_ace2s_pnw_output.yaml`. Specifically, update `experiment_dir`, `model.checkpoint_path`, and `data.coarse`. The directory path in `data.coarse` should point to the same ACE inference `experiment_dir` from step 3. Optionally, if you wish to change the region and/or time selection of the area(s) to downscale you may edit those in the downscaling config (see [downscaling inference docs](https://ai2-climate-emulator.readthedocs.io/en/latest/downscaling_inference.html) for more details). An example of global downscaling is also provided in `hiro_downscaling_ace2s_global_output.yaml`.

4. Run the script `run-hiro-ace.sh`.


### Strengths and weaknesses

#### ACE2S
The strengths of ACE2S are:
- stochastic (generative) emulator of 100km coarsened X-SHiELD 
- improved small scale variability compared to deterministic ACE2 (especially surface precipitation)
- exact conservation of global dry air mass and moisture
- low time-mean biases for most variables relative to the 10-year training data available
- compatible with HiRO model with no observed degradation

Some known weaknesses of ACE2S are:
- trained only on 9 years of X-SHiELD (2014-2022)
- not expected to generalize outside of the limited forcing conditions used during training (e.g., past/future SSTs and CO2)
- may not produce statistically independent and representative samples of the base X-SHiELD model when run for significantly longer than 9 years
- not suitable for weather forecasting (e.g., not trained on reanalysis data only climate model output)
- model was not tested as rigorously as ACE2-ERA5 (e.g., cannot comment on stratospheric variability, etc) 
- overestimation of tropical cyclone generation compared to X-SHiELD
- some aspects of the pre-training and fine-tuning methodologies did not have ablations
- exactly pre-training and fine-tuning methodologies are subject to potential significant changes for future versions

#### HiRO
The strengths of HiRO are:
- Quickly generates surface precipitation rates almost anywhere on the globe analogous to X-SHiELD 3 km output
- Reproduces surface precipitation rate distribution of X-SHiELD out to the 99.99th percentile
- Low time-mean biases against X-SHiELD
- Recovers time-mean characteristics of precipitation related to topography
- Stochastic for easy ensemble generation of small-scale variability

Some known weaknesses of HiRO are:
- Not expected to generalize to climates outside of the 10-year training dataset
- Only produces precipitation features representative of X-SHiELD data, not observations
- May not produce physically consistent precipitation features; different precipitation regimes (convective, stratiform, orographic) have not been rigorously analyzed separately
- Does not tend to produce the strongest isolated convection over tropical ocean regions of X-SHiELD (e.g., representative of 99.9999th percentile of X-SHiELD outputs)
- Not trained or tested outside of 66S -- 70N, and cannot run outside of 88S or 88N
- Downscaling regions larger than the 16x16 1-degree patch size used for training will have discontinuities for patch overlaps of 0 or some blending artifacting from averaging in the overlap region for overlaps > 0

## License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use).